The document “Sensor Network Localization via Received Signal Strength Measurements with Directional Antennas”, by Joshua N. Ash and Lee C. Potter, Dept. of Electrical and Computer Engineering, The Ohio State University, 2015 Neil Avenue, Columbus, Ohio 43210, relates to the self-localization that can be obtained using received signal strength (RSS) measurements from arrays of directional antennas on each sensor node. The Crame´r-Rao lower bound on location error variance is used to predict performance for efficient estimators and can inform design trade-offs for antennas, communication protocols, and estimation algorithms.
The document“Robust System Multiangulation Using Subspace Methods”, by Joshua N. Ash and Lee C. Potter, Department of Electrical and Computer Engineering, Ohio State University Columbus, Ohio 43210, relates to a robust and low-complexity algorithm to self-localize and orient sensors in a network based on angle-of-arrival (AOA) information.
The document J. Krumm: “Probabilistic Interferencing for Location”, Workshop on Location Aware Computing (Part of UlbiComp 2003), Oct. 12, 2003, Seattle, Wash., USA, describes and discusses various general techniques that researchers have adopted for processing sensor readings into location measurements, emphasizing probabilistic approaches. The described general techniques are deterministic function inversion, maximum likelihood estimate, MAP (maximum a posteriori) estimate, and three recursive filtering techniques: Kalman filter, hidden Markov model, and particle filter.
The Kalman filter assumes that the relationship between the measurement vector z and state vector x is linear with zero-mean, additive, Gaussian noise. It also assumes the previous state xt-1 and current state xt is linear with zero-mean, additive, Gaussian noice.
The patent document WO-2005/119293 A1 relates to a method for determining positional data of at least one node (K1) of a network, said method comprising a number of nodes (K1, . . . , Kn), whereby the positional data refers to an internal coordinate system. The method comprises the following steps: a) preparation of positional data for a subset (u) of nodes (K3, . . . , K6), b) determination of separation data (D1-3, . . . , D1-6) for the at least one node (K1), c) determination or, on repetition of step c), correction of the positional data for the at least one node (K1), depending on the positional data from step a), the separation data determined in step b) and positional data of the at least one node and d) repetition of steps a) to c), until an interruption condition is fulfilled.
The patent document WO-2006/002458 A1 relates inter alia to a method of providing enabled security services in a wireless network. The method comprises the steps of: receiving a network access request from a node requesting access to the wireless network; calculating a probability level for a position for the requesting node using position information claimed by the requesting node and position information about the requesting node derived from signal measurements for the requesting node received by at least one existing authorised node in the wireless network; and denying access for the requesting node to the wireless network if the probability level does not satisfy a specified threshold condition for network security. The position information claimed by the requesting node, the position information about the requesting node derived from signal measurements, or both, may comprise manually specified data for the respective node. The signal measurements may comprise received signal strength (RSS) measurements, time of arrival (TOA) measurements, time difference of arrival (TDOA), or angle of arrival (AOA) measurements.
The patent document U.S. Pat. No. 6,407,703 relates to a method for determining the geolocation of an emitter using sensors located at a single or multiple platforms. Generally, the method includes the steps of receiving a first measurement set relative to a first emitter, the first measurement set including angle of arrival, time difference of arrival and/or terrain height/altitude measurements, receiving a first location guess or estimate for the first emitter, and determining at least a second location estimate using at least one of a batch least squares analysis and a Kalman filter analysis.
The patent document US-2007/0180918 A1 relates to a self-organizing sensor network, wherein a number of sensor nodes organized themselves and include sensor elements, distance measuring elements and communication elements. The sensor network is able to locate individual, in particular mobile, sensor nodes. Each sensor node 1 includes inter alia a central processing unit 4, a communication means 5, and a distance measurement means 6. The distance measurement means 6 in the form of the radar module perform measurements to determine the distance to adjacent sensor nodes. By exchanging estimated positions via the communication means 5 and using suitable filtering and/or learning methods, such as a Kalman filter for example, the sensors can establish their location in an internal coordinate system.
The patent document US-2007/0060098 A1 relates to a radio frequency (RF) system and method for determining the location of a wireless node in a wireless mesh sensor network. The wireless network includes a plurality of wireless nodes linked to a digital computer, such as a server or a location processor, via a communications link. The method further comprises measuring the RF signal strength at the wireless nodes and/or differential time of arrivals of the received signals at the wireless nodes and/or the angle of arrival of the received signals at the wireless nodes. When RF signal strength and DTOA measurements can be utilized and the results of each can be optimally combined using least mean square (LMS), or Kalman, estimators to minimize any errors in the final calculated locations.
The patent document US-2007/0076638 A1 relates to efforts to determine the location of devices within a wireless network. An example system includes a wireless device that generates at least one pulse as a part of an output signal, and the at least one pulse is captured by anchor devices and used, in a time of arrival approach, to determine the location of the example device. Another example system includes an anchor node that generates a directional output signal, the direction output signal including data indicating its direction, and the directions of output signals from plural anchor nodes when pointed at a wireless device are used to determine the location of the wireless device. Combinations of the pulse and directional antenna systems, devices used within each of these systems, and approaches associated with these systems are also included.
The patent document US-2006/0215624 A1 relates to communications between network nodes on connected computer networks. Disclosed is a Neighbor Location Discovery Protocol (NLDP) that determines the relative locations of the nodes in a mesh network. NLDP can be implemented for an ad-hoc wireless network where the nodes are equipped with directional antennas and are not able to use GPS. While NLDP relies on nodes having at least two RF transceivers, it offers significant advantages over previously proposed protocols that employ only one RF transceiver. In NLDP antenna hardware is simple, easy to implement, and readily available. NLDP exploits the host node's ability to operate simultaneously over non-overlapping channels to quickly converge on the neighbour's location. NLDP is limited by the range of the control channel, which operates in an omni-directional fashion. However, by choosing a low frequency band, high power and low data rate, the range of the control channel can be increased to match the range on the data channel.
The patent document U.S. Pat. No. 6,618,690 relates to any system that estimates one or another aspect of the motion of an object, such as position. More particularly, the document pertains to the application of statistical filters, such as a Kalman filter, in such generalized positioning systems. The generalized positioning system uses a calculated association probability for each measurement in a set of measurements of positions (or other aspect of the motion) at a particular instant of time, the association probabilities being used in the calculation of a combined measurement innovation (residual), which is in turn used in calculating the next estimate of position (or other motion state information).
Some disadvantages with the above mentioned solutions are that they require centralized computation, are not robust against heavy noise in measurements, and convergence is not guaranteed.